Lanelet2: A high-definition map framework for the future of automated driving

Poggenhans, Fabian; Pauls, Jan-Hendrik; Janosovits, Johannes; Orf, Stefan; Naumann, Maximilian; Klunker, Florian; Mayr, Matthias

doi:10.1109/itsc.2018.8569929

Cited by 241 publications

(102 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Maps have become a very important component of autonomous driving systems [18]. High definition maps provide a lot of information about the surrounding environment of the vehicle, helping in some vital processes such global route calculation and local path planning.…”

Section: Map Creationmentioning

confidence: 99%

Framework for motion prediction of vehicles in a simulation environment

Lee¹,

Trentin²,

Villagrá³

2020

XL Jornadas De Automática: Libro De Actas (Ferrol, 4-6 De Septiembre De 2019)

View full text Add to dashboard Cite

Efficient testing and validation of software components for highly automate vehicles is one of the key challenges to be solved for their massive deployment. The number of driving situation and environment variables makes validation almost intractable with real vehicles in open roads, and the testing reproducibility can only be achieved via simulation. This manuscript presents a framework and preliminary results for motion prediction of vehicles in a simulation environment that is being currently developed by the AUTOPIA Program.

show abstract

Section: Map Creationmentioning

confidence: 99%

Framework for motion prediction of vehicles in a simulation environment

Lee¹,

Trentin²,

Villagrá³

2020

XL Jornadas De Automática: Libro De Actas (Ferrol, 4-6 De Septiembre De 2019)

View full text Add to dashboard Cite

show abstract

“…Each lane course is represented as a lanelet [21], which is comprised of a pair of two poly lines, the left and the right border. Each lane in the topology model, estimated in the first step, is converted into a lanelet representation with equidistantly distributed support points (see Figure 5).…”

Section: A Preprocessingmentioning

confidence: 99%

Anytime Lane-Level Intersection Estimation Based on Trajectories of Other Traffic Participants

Meyer

Walter

Lauer

et al. 2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

View full text Add to dashboard Cite

Estimating and understanding the current scene is an inevitable capability of automated vehicles. Usually, maps are used as prior for interpreting sensor measurements in order to drive safely and comfortably. Only few approaches take into account that maps might be outdated and lead to wrong assumptions on the environment. This work estimates a lanelevel intersection topology without any map prior by observing the trajectories of other traffic participants.We are able to deliver both a coarse lane-level topology as well as the lane course inside and outside of the intersection using Markov chain Monte Carlo sampling. The model is neither limited to a number of lanes or arms nor to the topology of the intersection.We present our results on an evaluation set of 1000 simulated intersections and achieve 99.9% accuracy on the topology estimation that takes only 36ms, when utilizing tracked object detections. The precise lane course on these intersections is estimated with an error of 15cm on average after 140ms. Our approach shows a similar level of precision on 14 realworld intersections with 18cm average deviation on simple intersections and 27cm for more complex scenarios. Here the estimation takes only 113ms in total.

show abstract

“…Whereas boundary lines are used to provide an abstract road network [10], the centerline of the road is also an important descriptor in lane-level road networks [11]. In general, a mobile mapping system (MMS) is often used to acquire precise road data.…”

Section: Lane-level Data Acquisition Of Road Geometrymentioning

confidence: 99%

Generation of Lane-Level Road Networks Based on a Trajectory-Similarity-Join Pruning Strategy

Zheng

Song²,

et al. 2019

IJGI

View full text Add to dashboard Cite

With the development of autonomous driving, lane-level maps have attracted significant attention. Since the lane-level road network is an important part of the lane-level map, the efficient, low-cost, and automatic generation of lane-level road networks has become increasingly important. We propose a new method here that generates lane-level road networks using only position information based on an autonomous vehicle and the existing lane-level road networks from the existing road-level professionally surveyed without lane details. This method uses the parallel relationship between the centerline of a lane and the centerline of the corresponding segment. Since the direct point-by-point computation is huge, we propose a method based on a trajectory-similarity-join pruning strategy (TSJ-PS). This method uses a filter-and-verify search framework. First, it performs quick segmentation based on the minimum distance and then uses the similarity of two trajectories to prune the trajectory similarity join. Next, it calculates the centerline trajectory for lanes using the simulation transformation model by the unpruned trajectory points. Finally, we demonstrate the efficiency of the algorithm and generate a lane-level road network via experiments on a real road.

show abstract

Lanelet2: A high-definition map framework for the future of automated driving

Cited by 241 publications

References 10 publications

Framework for motion prediction of vehicles in a simulation environment

Framework for motion prediction of vehicles in a simulation environment

Anytime Lane-Level Intersection Estimation Based on Trajectories of Other Traffic Participants

Generation of Lane-Level Road Networks Based on a Trajectory-Similarity-Join Pruning Strategy

Contact Info

Product

Resources

About